Fractional Differential Equation Physics-Informed Neural Network and Its Application in Battery State Estimation
This work addresses the problem of improving SOC estimation for lithium-ion battery systems, which is critical for safety and performance, and appears incremental as it builds on existing physics-informed neural networks by incorporating fractional calculus.
This study tackled the challenge of accurately estimating the State of Charge (SOC) in lithium-ion batteries by proposing the Fractional Differential Equation Physics-Informed Neural Network (FDIFF-PINN), which integrates fractional calculus with deep learning, and demonstrated its effectiveness through comparative experiments on a dynamic charge/discharge dataset under multi-temperature conditions.
Accurate estimation of the State of Charge (SOC) is critical for ensuring the safety, reliability, and performance optimization of lithium-ion battery systems. Conventional data-driven neural network models often struggle to fully characterize the inherent complex nonlinearities and memory-dependent dynamics of electrochemical processes, significantly limiting their predictive accuracy and physical interpretability under dynamic operating conditions. To address this challenge, this study proposes a novel neural architecture termed the Fractional Differential Equation Physics-Informed Neural Network (FDIFF-PINN), which integrates fractional calculus with deep learning. The main contributions of this paper include: (1) Based on a fractional-order equivalent circuit model, a discretized fractional-order partial differential equation is constructed. (2) Comparative experiments were conducted using a dynamic charge/discharge dataset of Panasonic 18650PF batteries under multi-temperature conditions (from -10$^{\circ}$C to 20$^{\circ}$C).